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In this course, you will :
- Learn about MLOps' core features. You'll learn about the machine learning lifecycle, including its phases and the roles connected with MLOps processes.
- Learn about the design and development stages of the machine learning lifecycle. You will learn about added value estimation, data quality, feature storage, and experiment tracking.
- Learn about runtime environments, containerization, CI/CD pipelines, and deployment methodologies that are important for deploying machine learning in production.
- Learn about maintaining machine learning in production, including statistical and computational monitoring, retraining, varying levels of MLOps maturity, and tools for streamlining procedures throughout the machine learning lifecycle.
Syllabus:
- Introduction to MLOps
- Design and Development
- Deploying Machine Learning into Production
- Maintaining Machine Learning in Production
